Uniform, Fast Convergence of Arbitrarily Tight Upper and Lower Bounds on the Bayes Error

Chen, Dechang; Fries, Michael A.; Xiang, YingChang

doi:10.1007/978-1-4613-0231-5_5

Dechang Chen⁵,
Michael A. Fries⁶ &
YingChang Xiang⁷

Part of the book series: Combinatorial Optimization ((COOP,volume 13))

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Abstract

This chapter studies the convergence behavior of the arbitrarily tight upper and lower bounds on the Bayes error proposed by Avi-Itzhak and Diep [1]. We show that these bounds converge to the Bayes error uniformly with a very fast convergence rate.

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References

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Authors and Affiliations

Department of Natural and Applied Sciences, University of Wisconsin, Green Bay, WI, 54311, USA
Dechang Chen
School of Computer Science, Telecommunications and Information Systems, DePaul University, Chicago, IL, 60604, USA
Michael A. Fries
Rizhao Vocational & Technical College, Yantai Road, Rizhao, Shandong, 276826, P. R. China
YingChang Xiang

Authors

Dechang Chen
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Michael A. Fries
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YingChang Xiang
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Editors and Affiliations

University of Wisconsin — Green Bay, Green Bay, WI, USA
Dechang Chen
The George Washington University, Washington DC, USA
Xiuzhen Cheng

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Chen, D., Fries, M.A., Xiang, Y. (2003). Uniform, Fast Convergence of Arbitrarily Tight Upper and Lower Bounds on the Bayes Error. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_5

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DOI: https://doi.org/10.1007/978-1-4613-0231-5_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7952-2
Online ISBN: 978-1-4613-0231-5
eBook Packages: Springer Book Archive

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